Liwei Song

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 6 papers; trusts 451 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 274 citations into it; #9,129 of 35,345 by trust here, #12,974 by citations
• Safety, Robustness & Interpretability — 189 citations into it; #2,072 of 2,251 by trust here, #1,616 by citations
• Computer Vision and Pattern Recognition — 85 citations into it
• Signal Processing — 48 citations into it
• Privacy & Security — 24 citations into it; #631 of 709 by trust here, #570 by citations
• Natural Language Processing — 23 citations into it
• Computer Networks and Communications — 18 citations into it
• Model Architectures — 18 citations into it

Whom Liwei Song trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Reza Shokri
5. Ilya Sutskever
6. Karen Simonyan
7. Yann LeCun
8. Yoshua Bengio
9. Diederik P. Kingma
10. Ian J. Goodfellow

Who trusts Liwei Song overall

1. Hongsheng Hu
2. Prateek Mittal
3. Nicholas Carlini
4. Lauren Watson
5. Anvith Thudi
6. Reza Shokri
7. Zoran A. Salcic
8. Weichen Yu
9. Chuan Guo
10. Stephen Casper

Papers from Liwei Song's perspective

Ordered by how strongly the researchers Liwei Song trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Towards Probabilistic Verification of Machine Unlearning (2020) — 86 citations
• Universal Adversarial Attacks with Natural Triggers for Text Classification (2020) — 88 citations
• Systematic Evaluation of Privacy Risks of Machine Learning Models (2020) — 510 citations
• Privacy Risks of Securing Machine Learning Models against Adversarial Examples (2019) — 304 citations
• Membership Inference Attacks Against Adversarially Robust Deep Learning Models (2019) — 102 citations
• Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples (2019) — 27 citations